OpenRaiser/Intern-Atlas huggingface dataset:
Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest
Venue: arXiv cs.HC 2017
Year: 2017

Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment -- e.g. game score, completion time, etc. -- in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective -- e.g. human preferences for certain AI behavior -- in order to adapt the AI behavior to idiosyncratic human preferences. Such adaptations would mirror naturally occurring processes that increase trust and comfort during social interactions. Here, we show how a hybrid brain-computer-interface (hBCI), which detects an individual's level of interest in objects/events in a virtual environment, can be used to adapt the behavior of a Deep Reinforcement Learning AI agent that is controlling a virtual autonomous vehicle. Specifically, we show that the AI learns a driving strategy that maintains a safe distance from a lead vehicle, and most novelly, preferentially slows the vehicle when the human passengers of the vehicle encounter objects of interest. This adaptation affords an additional 20\% viewing time for subjectively interesting objects. This is the first demonstration of how an hBCI can be used to provide implicit reinforcement to an AI agent in a way that incorporates user preferences into the control system.
Simple Round Compression for Parallel Vertex Cover
Venue: arXiv cs.DS 2017
Year: 2017

Recently, Czumaj et.al. (arXiv 2017) presented a parallel (almost) $2$-approximation algorithm for the maximum matching problem in only $O({(\log\log{n})^2})$ rounds of the massive parallel computation (MPC) framework, when the memory per machine is $O(n)$. The main approach in their work is a way of compressing $O(\log{n})$ rounds of a distributed algorithm for maximum matching into only $O({(\log\log{n})^2})$ MPC rounds. In this note, we present a similar algorithm for the closely related problem of approximating the minimum vertex cover in the MPC framework. We show that one can achieve an $O(\log{n})$ approximation to minimum vertex cover in only $O(\log\log{n})$ MPC rounds when the memory per machine is $O(n)$. Our algorithm for vertex cover is similar to the maximum matching algorithm of Czumaj et.al. but avoids many of the intricacies in their approach and as a result admits a considerably simpler analysis (at a cost of a worse approximation guarantee). We obtain this result by modifying a previous parallel algorithm by Khanna and the author (SPAA 2017) for vertex cover that allowed for compressing $O(\log{n})$ rounds of a distributed algorithm into constant MPC rounds when the memory allowed per machine is $O(n\sqrt{n})$.
Detection of Unauthorized IoT Devices Using Machine Learning Techniques
Venue: arXiv cs.CR 2017
Year: 2017

Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption of such devices, their diversity, standardization obstacles, and inherent mobility, organizations require an intelligent mechanism capable of automatically detecting suspicious IoT devices connected to their networks. In particular, devices not included in a white list of trustworthy IoT device types (allowed to be used within the organizational premises) should be detected. In this research, Random Forest, a supervised machine learning algorithm, was applied to features extracted from network traffic data with the aim of accurately identifying IoT device types from the white list. To train and evaluate multi-class classifiers, we collected and manually labeled network traffic data from 17 distinct IoT devices, representing nine types of IoT devices. Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases. Some IoT device types were identified quicker than others (e.g., sockets and thermostats were successfully detected within five TCP sessions of connecting to the network). Perfect detection of unauthorized IoT device types was achieved upon analyzing 110 consecutive sessions; perfect classification of white listed types required 346 consecutive sessions, 110 of which resulted in 99.49% accuracy. Further experiments demonstrated the successful applicability of classifiers trained in one location and tested on another. In addition, a discussion is provided regarding the resilience of our machine learning-based IoT white listing method to adversarial attacks.
Towards an Arabic-English Machine-Translation Based on Semantic Web
Venue: arXiv cs.CL 2017
Year: 2017

Communication tools make the world like a small village and as a consequence people can contact with others who are from different societies or who speak different languages. This communication cannot happen effectively without Machine Translation because they can be found anytime and everywhere. There are a number of studies that have developed Machine Translation for the English language with so many other languages except the Arabic it has not been considered yet. Therefore we aim to highlight a roadmap for our proposed translation machine to provide an enhanced Arabic English translation based on Semantic.
Machine-Translation History and Evolution: Survey for Arabic-English Translations
Venue: arXiv cs.CL 2017
Year: 2017

As a result of the rapid changes in information and communication technology (ICT), the world has become a small village where people from all over the world connect with each other in dialogue and communication via the Internet. Also, communications have become a daily routine activity due to the new globalization where companies and even universities become global residing cross countries borders. As a result, translation becomes a needed activity in this connected world. ICT made it possible to have a student in one country take a course or even a degree from a different country anytime anywhere easily. The resulted communication still needs a language as a means that helps the receiver understands the contents of the sent message. People need an automated translation application because human translators are hard to find all the times, and the human translations are very expensive comparing to the translations automated process. Several types of research describe the electronic process of the Machine-Translation. In this paper, the authors are going to study some of these previous researches, and they will explore some of the needed tools for the Machine-Translation. This research is going to contribute to the Machine-Translation area by helping future researchers to have a summary for the Machine-Translation groups of research and to let lights on the importance of the translation mechanism.
Codes for Erasures over Directed Graphs
Venue: arXiv cs.IT 2017
Year: 2017

In this work we continue the study of a new class of codes, called \emph{codes over graphs}. Here we consider storage systems where the information is stored on the edges of a complete directed graph with $n$ nodes. The failure model we consider is of \emph{node failures} which are erasures of all edges, both incoming and outgoing, connected to the failed node. It is said that a code over graphs is a \textit{$ρ$-node-erasure-correcting code} if it can correct the failure of any $ρ$ nodes in the graphs of the code. While the construction of such optimal codes is an easy task if the field size is ${\cal O} (n^2)$, our main goal in the paper is the construction of codes over smaller fields. In particular, our main result is the construction of optimal binary codes over graphs which correct two node failures with a prime number of nodes.
Embedded-Graph Theory
Venue: arXiv cs.DM 2017
Year: 2017

In this paper, we propose a new type of graph, denoted as "embedded-graph", and its theory, which employs a distributed representation to describe the relations on the graph edges. Embedded-graphs can express linguistic and complicated relations, which cannot be expressed by the existing edge-graphs or weighted-graphs. We introduce the mathematical definition of embedded-graph, translation, edge distance, and graph similarity. We can transform an embedded-graph into a weighted-graph and a weighted-graph into an edge-graph by the translation method and by threshold calculation, respectively. The edge distance of an embedded-graph is a distance based on the components of a target vector, and it is calculated through cosine similarity with the target vector. The graph similarity is obtained considering the relations with linguistic complexity. In addition, we provide some examples and data structures for embedded-graphs in this paper.
EMOMA: Exact Match in One Memory Access
Venue: arXiv cs.DS 2017
Year: 2017

An important function in modern routers and switches is to perform a lookup for a key. Hash-based methods, and in particular cuckoo hash tables, are popular for such lookup operations, but for large structures stored in off-chip memory, such methods have the downside that they may require more than one off-chip memory access to perform the key lookup. Although the number of off-chip memory accesses can be reduced using on-chip approximate membership structures such as Bloom filters, some lookups may still require more than one off-chip memory access. This can be problematic for some hardware implementations, as having only a single off-chip memory access enables a predictable processing of lookups and avoids the need to queue pending requests. We provide a data structure for hash-based lookups based on cuckoo hashing that uses only one off-chip memory access per lookup, by utilizing an on-chip pre-filter to determine which of multiple locations holds a key. We make particular use of the flexibility to move elements within a cuckoo hash table to ensure the pre-filter always gives the correct response. While this requires a slightly more complex insertion procedure and some additional memory accesses during insertions, it is suitable for most packet processing applications where key lookups are much more frequent than insertions. An important feature of our approach is its simplicity. Our approach is based on simple logic that can be easily implemented in hardware, and hardware implementations would benefit most from the single off-chip memory access per lookup.
Trace and Stable Failures Semantics for CSP-Agda
Venue: arXiv cs.PL 2017
Year: 2017

CSP-Agda is a library, which formalises the process algebra CSP in the interactive theorem prover Agda using coinductive data types. In CSP-Agda, CSP processes are in monadic form, which sup- ports a modular development of processes. In this paper, we implement two main models of CSP, trace and stable failures semantics, in CSP-Agda, and define the corresponding refinement and equal- ity relations. Because of the monadic setting, some adjustments need to be made. As an example, we prove commutativity of the external choice operator w.r.t. the trace semantics in CSP-Agda, and that refinement w.r.t. stable failures semantics is a partial order. All proofs and definitions have been type checked in Agda. Further proofs of algebraic laws will be available in the CSP-Agda repository.
Perspectives for Evaluating Conversational AI
Venue: arXiv cs.AI 2017
Year: 2017

Conversational AI systems are becoming famous in day to day lives. In this paper, we are trying to address the following key question: To identify whether design, as well as development efforts for search oriented conversational AI are successful or not.It is tricky to define 'success' in the case of conversational AI and equally tricky part is to use appropriate metrics for the evaluation of conversational AI. We propose four different perspectives namely user experience, information retrieval, linguistic and artificial intelligence for the evaluation of conversational AI systems. Additionally, background details of conversational AI systems are provided including desirable characteristics of personal assistants, differences between chatbot and an AI based personal assistant. An importance of personalization and how it can be achieved is explained in detail. Current challenges in the development of an ideal conversational AI (personal assistant) are also highlighted along with guidelines for achieving personalized experience for users.
T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark
Venue: arXiv cs.DB 2017
Year: 2017

Information retrieval from textual data focuses on the construction of vocabularies that contain weighted term tuples. Such vocabularies can then be exploited by various text analysis algorithms to extract new knowledge, e.g., top-k keywords, top-k documents, etc. Top-k keywords are casually used for various purposes, are often computed on-the-fly, and thus must be efficiently computed. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present a top-k keywords benchmark, T${}^2$K${}^2$, which features a real tweet dataset and queries with various complexities and selectivities. T${}^2$K${}^2$ helps evaluate weighting schemes and database implementations in terms of computing performance. To illustrate T${}^2$K${}^2$'s relevance and genericity, we successfully performed tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand.
The wave method of building color palette and its application in computer graphics
Venue: arXiv cs.GR 2017
Year: 2017

This article describes a method of getting a harmonious combination of colors, developed by us on the basis of the relationship of color and acoustic waves. Presents a parallel between harmoniously matched colors and the concept of harmony in music theory (consonance). Describes the physical assumption of the essence of the phenomenon of harmony (consonance). The article also provides algorithm of implementation wave method for the sRGB color model.
A review of approaches to the value of privacy
Venue: arXiv cs.SI 2017
Year: 2017

In this paper we examine information privacy from a value-centered angle. We review and compare different personal data valuation approaches. We consider the value of personal data now and in the near future, and we find that the enduring part of personal data will soon become common knowledge and that their price and value will drop substantially. Therefore the sector that is based on personal data extraction will need to focus on new ways of monetization and on the of the dynamic part of personal information.
Diffusion Based Molecular Communication: Principle, Key Technologies, and Challenges
Venue: arXiv cs.IT 2017
Year: 2017

Molecular communication (MC) is a kind of communication technology based on biochemical molecules for internet of bio-nano things, in which the biochemical molecule is used as the information carrier for the interconnection of nano-devices. In this paper, the basic principle of diffusion based MC and the corresponding key technologies are comprehensively surveyed. In particular, the state-of-the-art achievements relative to the diffusion based MC are discussed and compared, including the system model, the system performance analysis with key influencing factors, the information coding and modulation techniques. Meanwhile, the multi-hop nano-network based on the diffusion MC is presented as well. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of channel model, information theory, self-organizing nano-network, and biochemical applications are put forward.
Scheduling strategies and throughput optimization for the Downlink for IEEE 802.11ax and IEEE 802.11ac based networks
Venue: arXiv cs.NI 2017
Year: 2017

The new IEEE 802.11 standard, IEEE 802.11ax, has the challenging goal of serving more users compared to its predecessor IEEE 802.11ac, enabling consistent and reliable streams of data (average throughput) per station. In this paper we explore some of the IEEE 802.11ax new mechanisms and compare between the upper bounds on the throughputs of the Downlink unidirectional UDP Multi Users (MU) triadic based on Multiple-Input-Multiple-Output (MU-MIMO) and Orthogonal Frequency Division Multiple Access (OFDMA) transmission multiplexing format in IEEE 802.11ax vs. IEEE 802.11ac in the Single User (SU) and MU modes for 1, 4, 8, 16, 32 and 64 stations scenario in reliable and unreliable channels. The comparison is made as a function of the Modulation and Coding Schemes (MCS) in use. In IEEE 802.11ax we consider two flavors of acknowledgment operation settings where the maximum acknowledgment windows are 64 or 256 respectively. In SU scenario IEEE 802.11ax upper bounds on the throughputs outperform IEEE 802.11ac by about 52% and 74% in reliable and unreliable channels respectively. In MU-MIMO scenario IEEE 802.11ax upper bounds on the throughputs outperform IEEE 802.11ac by about 59% and 103% in reliable and unreliable channels respectively. Also, as the number of stations increases, the advantage of IEEE 802.11ax in terms of the access delay also increases.
General problem solving with category theory
Venue: arXiv cs.AI 2017
Year: 2017

This paper proposes a formal cognitive framework for problem solving based on category theory. We introduce cognitive categories, which are categories with exactly one morphism between any two objects. Objects in these categories are interpreted as states and morphisms as transformations between states. Moreover, cognitive problems are reduced to the specification of two objects in a cognitive category: an outset (i.e. the current state of the system) and a goal (i.e. the desired state). Cognitive systems transform the target system by means of generators and evaluators. Generators realize cognitive operations over a system by grouping morphisms, whilst evaluators group objects as a way to generalize outsets and goals to partially defined states. Meta-cognition emerges when the whole cognitive system is self-referenced as sub-states in the cognitive category, whilst learning must always be considered as a meta-cognitive process to maintain consistency. Several examples grounded in basic AI methods are provided as well.
Benchmarking Super-Resolution Algorithms on Real Data
Venue: arXiv cs.CV 2017
Year: 2017

Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variations.
Convolutional Networks for Spherical Signals
Venue: arXiv cs.LG 2017
Year: 2017

The success of convolutional networks in learning problems involving planar signals such as images is due to their ability to exploit the translation symmetry of the data distribution through weight sharing. Many areas of science and egineering deal with signals with other symmetries, such as rotation invariant data on the sphere. Examples include climate and weather science, astrophysics, and chemistry. In this paper we present spherical convolutional networks. These networks use convolutions on the sphere and rotation group, which results in rotational weight sharing and rotation equivariance. Using a synthetic spherical MNIST dataset, we show that spherical convolutional networks are very effective at dealing with rotationally invariant classification problems.
Recent Advances in Fog Radio Access Networks: Performance Analysis and Radio Resource Allocation
Venue: arXiv cs.IT 2017
Year: 2017

As a promising paradigm for the fifth generation wireless communication (5G) system, the fog radio access network (F-RAN) has been proposed as an advanced socially-aware mobile networking architecture to provide high spectral efficiency (SE) while maintaining high energy efficiency (EE) and low latency. Recent advents are advocated to the performance analysis and radio resource allocation, both of which are fundamental issues to make F-RANs successfully rollout. This article comprehensively summarizes the recent advances of the performance analysis and radio resource allocation in F-RANs. Particularly, the advanced edge cache and adaptive model selection schemes are presented to improve SE and EE under maintaining a low latency level. The radio resource allocation strategies to optimize SE and EE in F-RANs are respectively proposed. A few open issues in terms of the F-RAN based 5G architecture and the social-awareness technique are identified as well.
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification
Venue: arXiv cs.CV 2017
Year: 2017

We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical inter-class structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. Experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification.